class_docstr: Implementation of methods in Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks.
  Wang et al. (2019).
  
  | Paper link: https://people.cs.uchicago.edu/~ravenben/publications/pdf/backdoor-sp19.pdf
class_docstr_tok: ["Implementation","of","methods","in","Neural","Cleanse",":","Identifying","and","Mitigating","Backdoor","Attacks","in","Neural","Networks",".","Wang","et","al",".","(","2019",")",".","|","Paper","link",":","https",":","\/\/people.cs.uchicago.edu\/~ravenben\/publications\/pdf\/backdoor-sp19.pdf"]
docstr: Generates a possible backdoor for the model. Returns the pattern and the mask
  :return: A tuple of the pattern and mask for the model.
docstr_tok: ["Generates","a","possible","backdoor","for","the","model",".","Returns","the","pattern","and","the","mask",":","return",":","A","tuple","of","the","pattern","and","mask","for","the","model","."]
code: def generate_backdoor(
      self, x_val: np.ndarray, y_val: np.ndarray, y_target: np.ndarray
  ) -> Tuple[np.ndarray, np.ndarray]:
      """
      Generates a possible backdoor for the model. Returns the pattern and the mask
      :return: A tuple of the pattern and mask for the model.
      """
      raise NotImplementedError
  
file_path: adversarial-robustness-toolbox/art/estimators/poison_mitigation/neural_cleanse/neural_cleanse.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Union, Tuple, List
  import numpy
  from art.estimators.certification.abstain import AbstainPredictorMixin
  from art.utils import to_categorical
label_desc: Use image node_id 10 for calling the NeuralCleanseMixin obj's underlying member method code with example usage: obj.generate_backdoor(x_val, y_val, y_target) without return types